Projecting images captured using fisheye lenses for feature detection in autonomous machine applications

ABSTRACT

In various examples, live perception from wide-view sensors may be leveraged to detect features in an environment of a vehicle. Sensor data generated by the sensors may be adjusted to represent a virtual field of view different from an actual field of view of the sensor, and the sensor data—with or without virtual adjustment—may be applied to a stereographic projection algorithm to generate a projected image. The projected image may then be applied to a machine learning model—such as a deep neural network (DNN)—to detect and/or classify features or objects represented therein. In some examples, the machine learning model may be pre-trained on training sensor data generated by a sensor having a field of view less than the wide-view sensor such that the virtual adjustment and/or projection algorithm may update the sensor data to be suitable for accurate processing by the pre-trained machine learning model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/814,577, filed Apr. 6, 2020, which is hereby incorporated byreference in its entirety.

BACKGROUND

Autonomous driving systems and advanced driver assistance systems (ADAS)may leverage various sensors to perform various tasks—such as lanekeeping, lane changing, lane assignment, camera calibration, turning,stopping, path planning, and localization. For example, for autonomousand ADAS systems to operate independently and efficiently, anunderstanding of the surrounding environment of the vehicle in real-timeor near real-time may be generated. This understanding may includeinformation as to locations of objects, obstacles, lane markers, signs,and/or traffic lights in the environment, which provide context andvisual indicia for various demarcations, such as lanes, road boundaries,intersections, and/or the like. The information of the surroundingenvironment may be used by a vehicle when making decisions, such as apath to travel in view of the various objects (e.g., vehicles,pedestrians, bicyclists, etc.) in the environment, when and if to changelanes, how fast to drive, where to stop at an intersection, and/or thelike.

As an example, information regarding locations and attributes of objectsand/or lanes in an environment of an autonomous or semi-autonomousvehicle may prove valuable when performing path planning, obstacleavoidance, and/or control decisions. Machine learning models and/orcomputer vision algorithms are often trained or programmed to generateinformation of the surrounding environment of a vehicle. For example,these machine learning models (e.g., deep neural networks (DNNs)) and/orcomputer vision algorithms are trained to generate an understanding ofthe surrounding environment represented by sensor data (e.g., images)generated by sensors with varying fields of view. For example, many DNNsmay be trained using sensors (e.g., image sensors) with fields of viewbetween 60 and 120 degrees. However, at least some sensors (e.g.,cameras, LIDAR sensors, RADAR sensors, etc.) of a vehicle may have afield of view greater than 120 degrees—such as parking cameraspositioned to a rear or front of a vehicle, or side-view cameraspositioned on side-view mirrors of a vehicle. For example, parkingcameras often employ fisheye cameras with fields of view of upwards ofor greater than 190 degrees. As a result, image data generated by theseimage sensors having wide fields of view may not be suitable forprocessing by DNNs—e.g., due to distortion, artifacts, and/or otherimperfections.

In conventional systems, objects of a vehicle's environment may bedetected using a DNN trained to detect features represented by trainingimage data generated using image sensors having fields of view of lessthan 120 degrees. For example, pinhole cameras—often used forforward-facing cameras (known as “dash cameras” or “dash cams,”colloquially) and rear-facing cameras—are examples of image sensors witha typically narrower fields of view. Images captured by such imagesensors have minimal distortion, as the images are largely rectilinear,and images generated by such cameras may be used to train the DNN todetect features of an environment. However, image data captured byparking cameras (e.g., fisheye cameras) with a higher degree field ofview (e.g., greater than 120 degrees) may include distortion in areaswhere limited information is available (e.g., edges of the images), andthus may result in inaccurate computations by a DNN. As such,conventional approaches may retrain the DNN using image data captured bysensors with greater fields of view, which not only requires significantcomputational cost and manual effort, but also limits the scalabilityand adaptability of the DNN for sensor data representing smaller fieldsof view (e.g., less than 120 degrees). As a result, these conventionalsystems require training various instances of DNNs such that eachinstance of the DNN corresponds to a particular field of view—e.g., afirst instance for a field of view of 90-120 degrees and a secondinstance for a field of view of 120-180 degrees. In addition, even wherethe DNN is trained specifically for wider fields of view, due to thevariance in both scales and angles of distortion in image data fromwide-view sensors, labeling features for ground truth may be difficultdue to the skewed orientation of the features (e.g., object, lines) inthe image. For example, features such as lines and shapes in image-spacemay not align with the locations of the same in world-space, addinganother layer of complexity for the DNN and/or post-processing toaccurately coordinate outputs of the DNN with real-world locations offeatures. Without an accurate mapping of outputs to world-spacelocations, the DNN may not be as reliable for performing operations in atechnology space as safety critical as autonomous driving.

SUMMARY

Embodiments of the present disclosure relate to stereographicallyprojecting images captured using fisheye lenses for feature detectionusing neural networks. Systems and methods are disclosed that leverageexisting neural networks trained on outputs captured using narrowerfield of view sensors (e.g., sensors with fields of view less than 120degrees) to detect features in outputs from wider field of view sensors(e.g., sensors with fields of view greater than 120 degrees) inreal-time or near real-time.

In contrast to conventional systems, such as those described above, thesystems and methods of the present disclosure may leverage liveperception of wide field of view sensors (e.g., greater than 120degrees) to detect one or more features in a vehicle's environment. Forexample, an image from a wide field of view sensor may be applied to astereographic projection algorithm to project the image onto atwo-dimensional (2D) plane. The projected image may then be leveraged todetect features in the vehicle's environment using a neural networktrained to detect features in images captured by narrower field of viewsensors. In some examples, the field of view of the wide field of viewsensor may be virtually adjusted to generate an updated image with thevirtually adjusted field of view prior to applying the image to thestereographic projection algorithm. The field of view of the wide fieldof view sensor may be vertically adjusted such that the virtual centerof the sensor (e.g., a camera center-point) substantially aligns with ahorizon. In some other examples, the detected features may be convertedto image-space locations and corresponding world-space locations. Forexample, the outputs may be used to directly or indirectly (e.g., viadecoding) to determine locations of each feature, classification of eachfeature, and/or the like.

As a result of using existing neural networks—e.g., DNNs trained usinglower field of view images—to detect features in outputs of high fieldof view images, the additional compute and time resources for training anew neural network or retraining of the pre-trained neural network forwide field of view image sensors is not required. As such, the processof detecting features in images captured using wide field of viewsensors may be comparatively less time-consuming, less computationallyintense, and more scalable as the system may learn to detect features inreal-time or near real-time, without requiring prior experience,training, or knowledge of the environment and the field of view of thesensors.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for stereographically projecting imagescaptured using fisheye lenses for feature detection are described indetail below with reference to the attached drawing figures, wherein:

FIG. 1A is an example data flow diagram illustrating an example processfor detecting features of a vehicle's environment using outputs from oneor more sensors of the vehicle, in accordance with some embodiments ofthe present disclosure;

FIG. 1B depicts an illustration of an example of projecting an originalimage onto a 2D projection plane using a virtual sphere to generate aprojected image, in accordance with some embodiments of the presentdisclosure;

FIG. 2 depicts an illustration of example fields of view of wide fieldof view sensors on a vehicle, in accordance with some embodiments of thepresent disclosure;

FIG. 3 depicts an illustration of example distortion representationscorresponding to three sensors with different fields of view, inaccordance with some embodiments of the present disclosure;

FIG. 4 depicts an illustration of an example image captured with avirtually adjusted using field of view, in accordance with someembodiments of the present disclosure;

FIG. 5 depicts an illustration of example object detections in aprojected image, in accordance with some embodiments of the presentdisclosure;

FIG. 6 is a flow diagram illustrating an example process for detectingfeatures in images captured by wide field of view sensors using anexisting neural network trained on images captured using narrower fieldof view sensors, in accordance with some embodiments of the presentdisclosure;

FIG. 7 is a flow diagram illustrating an example process for detectingfeatures in virtually adjusted projected images captured by wide fieldof view sensors, in accordance with some embodiments of the presentdisclosure;

FIG. 8A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 8B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 8A, in accordance with someembodiments of the present disclosure;

FIG. 8C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 8A, in accordance with someembodiments of the present disclosure;

FIG. 8D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 8A, in accordancewith some embodiments of the present disclosure; and

FIG. 9 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to stereographicallyprojecting images captured using fisheye lenses for feature detectionusing neural networks. Although the present disclosure may be describedwith respect to an example autonomous vehicle 800 (alternativelyreferred to herein as “vehicle 800” or “ego-vehicle 800,” an example ofwhich is described with respect to FIGS. 8A-8D), this is not intended tobe limiting. For example, the systems and methods described herein maybe used by, without limitation, non-autonomous vehicles, semi-autonomousvehicles (e.g., in one or more adaptive driver assistance systems(ADAS)), robots, warehouse vehicles, off-road vehicles, flying vessels,boats, shuttles, emergency response vehicles, motorcycles, electric ormotorized bicycles, aircraft, construction vehicles, underwater craft,drones, and/or other vehicle types. In addition, although the presentdisclosure may be described with feature detection and classificationfor vehicle applications, this is not intended to be limiting, and thesystems and methods described herein may be used in augmented reality,virtual reality, robotics, security and surveillance, autonomous orsemi-autonomous machine applications, and/or any other technology spaceswhere wide field of view sensors are deployed and may be leveraged forperforming various operations—e.g., object and/or feature detection orclassification.

As described herein, in contrast to conventional approaches, the currentsystems and methods provide techniques to detect features in imagescaptured by wide field of view sensors (e.g., sensors with fields ofview greater than 120 degrees) using—in embodiments—an existing deepneural network (DNN) trained on images captured using narrower field ofview sensors (e.g., sensors with fields of view less than 120 degrees)in real-time or near real-time. As such, live perception from wide fieldof view sensors may be leveraged to detect features (e.g., objects,lanes, pedestrians) in images of the environment of the vehicle. In someembodiments, image data from a wide field of view sensor may beprojected onto a two-dimensional target plane to generatestereographically projected images that have lower distortions whereless information is available—e.g., on the edges of the images. Theimage data, after stereographic projection, may be applied to anexisting DNN that was trained on sensor data representative of narrowerfields of view, such that retraining of the DNN for the wide-view imagesensor is not required. In other embodiments, however, may omit apre-trained DNN by training a DNN on projected—and/or virtually adjustedimages (e.g., images with virtually-adjusted fields of view), withoutdeparting from the scope of the present disclosure.

In some embodiments, in order to accurately project highly distortedareas as captured in the image data, the field of view of the imagesrepresented by the image data may be virtually adjusted prior toapplying the image data to a stereographic projection algorithm. Forexample, the field of view of the image sensor may be adjusted upwards(e.g., because parking image sensors or side-view image sensors may beangled downward and toward a ground plane). In some embodiments, thefield of view may be adjusted (e.g., rotated) such that a virtual centerof the image sensor substantially aligns with a horizon (e.g., a horizonof the real-world). For example, a rotation amount may be determinedbased on an analysis of a mounting angle of the image sensor, ananalysis of various images captured using the image sensor, and/or otherinformation. As such, a predetermined angle of adjustment may bedetermined for the virtual adjusted field of view. In some embodiments,this adjustment may correspond to substantially aligning a point (e.g.,a camera center) on the image sensor with a horizon in the environment.In some embodiments, the horizon may correspond to a bounding linedemarcating where a driving surface intersects the sky in an image. Insuch embodiments, the adjustment to the field of view may include, forone or more calibration images, adjusting the field of view until thepoint on the image sensor substantially aligns with a horizon asrepresented in the one or more calibration images. Once this alignmentis determined, the angle of adjustment may be determined (e.g., byaveraging the angle over any number of the calibration images), and thedetermined angle of adjustment may be used as the virtual adjustment tothe field of view. By virtually adjusting the field of view, pixels inthe highly distorted regions of the image where valuable information isrepresented may become less distorted, and portions of the image datawhere more information is available (e.g., a center of the image) may bemore distorted—but the amount of information available at a center ofthe image even after distortion may be enough for accurate processing.

In addition, in some embodiments, the feature detections may be used todetermine locations of the features and/or objects in world-space. Insuch examples, image-space locations may be converted to world-spacelocations using sensor calibrations—e.g., intrinsic and/or extrinsicproperties of the sensors. The world-space locations of the featuresand/or objects may then be used by various other systems of the vehiclein performing path planning, obstacle avoidance, control decisions,and/or other autonomous or semi-autonomous operations.

As such, live perception of high field of view cameras may be leveragedto generate an understanding of a vehicle's environment using anexisting DNN—e.g., without requiring retraining a DNN or training a newDNN. As such, the amount of compute power and manual effort required toperform object detection, feature detection, and/or other computationsusing the sensor data from a wide-view sensor may be drastically reducedas compared to conventional systems. In addition, even where a DNN istrained on the projected and/or virtually adjusted field of view images,the results may be more accurate and reliable as compared toconventional systems that attempt to train the DNN on non-projectedand/or unadjusted fields of view.

Object and Feature Detection System

At a high level, sensor data (e.g., image data, LIDAR data, RADAR data,etc.) may be received and/or generated using sensors (e.g., cameras,RADAR sensors, LIDAR sensors, etc.) located or otherwise disposed on anautonomous or semi-autonomous vehicle. The sensors may be wide field ofview sensors (e.g., sensors with a field of view equal to or greaterthan 120 degrees). The sensor data may be applied to a projectionalgorithm—e.g., a stereographic projection algorithm, a gnomonicprojection algorithm, etc.—that is trained and/or programmed to generateprojected sensor data representative of a projected image. Where astereographic projection algorithm is used, the stereographic projectionalgorithm may project pixels of the sensor data onto a projection planebased on ray formulations over a virtual sphere depicting a field ofview of the wide field of view sensor. The projected sensor data may beapplied to a neural network (e.g., a deep neural network (DNN), such asa convolutional neural network (CNN)) that is trained to identify areasof interests pertaining to, as non-limiting examples, objects in theenvironment (e.g., vehicles, pedestrians, etc.), features of theenvironment (e.g., raised pavement markers, rumble strips, colored lanedividers, sidewalks, cross-walks, turn-offs, road layouts, objects,etc.), and/or semantic information (e.g., wait conditions, object types,lane types) pertaining thereto. In some examples, image-space locationsof the detected objects or features may also be calculated by the DNN.As described herein, the DNN may be trained (e.g., pre-trained) withimages or other sensor data representations captured from narrower fieldof view sensors (e.g., sensors with a field of view less than 120degrees).

In some embodiments, gnomonic projection algorithms may be used toperform the projection of the sensor data to generate projected sensordata for use by the DNN. However, gnomonic projection may not be asaccurate for wide-view sensors as gnomonic project may fail or be lessreliable for fields of view greater than 90 degrees, where theprojections of certain pixels on the image data may reach infinity onthe target plane. As such, to account for this, subsets of the imagedata may be projected onto different planes to account for a wider fieldof view. However, such partial projection may be computationallyexpensive as at least four planes may be needed to cover the entirefield of view. Further, features may end up with portions in differentprojected planes, thereby resulting in inaccurate outputs by the DNN.

As a result, in some embodiments, a stereographic projection algorithmmay be used to project pixels of an image onto a target plane (e.g., atwo-dimensional plane). A virtual sphere may be used as a virtual fieldof view of the sensor, and the lowest point on the virtual sphere may beused as a center of the projection to project the image onto the targetplane. For each pixel of the target plane (e.g., projected image), apoint on the sphere may be determined to be projected onto that pixelbased on the intersection of a virtual line between the center of theprojection and the pixel on the target plane with a point (e.g., pixel)on the virtual sphere. In this way, every pixel on the target plane orthe projected image corresponds to a pixel sampled on the originalimage. By generating a projected image in this way, each original imageis fully captured on a two-dimensional plane such that the projectedimage is invertible, where a feature detected by the neural network onthe projected image may be retraced to the original image using a rayformulation to determine a location of the feature on the originalimage. Further, the stereographic projection algorithm may projectimages captured by wide field of view sensors onto a single plane,thereby comparatively reducing the computational expense compared tognomonic projection techniques that divide the images into variousportions and subsequently project the divided portions on multipleplanes. The projected image may include a planar view of the originalimages captured by sensors with fields of view between 120 degrees and360 degrees by preserving areas of interest that had the most distortionin the original, un-projected image.

In some examples, the sensor data may undergo pre-processing tovirtually adjust the field of view of the wide field of view sensor togenerate an updated image such as the most distorted—and potentiallymost informative—areas of the images (e.g., edges) may be relocated toareas of the virtual sphere where the pixel information is mostpreserved during projection. For example, the virtual field of view ofthe wide field of view sensor may be adjusted in a vertical directionsuch that the virtual center of the sensor substantially aligns with ahorizon of the real world. In such examples, the virtual center of theimage may be moved vertically upwards by rotating the virtual sphere bya predetermined degree. The predetermined degree may be based on asensor calibration and may be, as a non-limiting example, between 20 and60 degrees. For example, depending on the location and angle of thesensor on the vehicle, the degree of rotation may be determined. In someexamples, the virtual adjustment may be generated by rotating rays thatform the original image by the predetermined degree when projecting theimage onto the projected plane. The updated image may be applied to thestereographic projection algorithm to generate the projected image forapplying to the DNN. Aligning the virtual center of the sensor with thehorizon may allow the projection to be aligned with a constant for allsensor data generated by the sensor, and may allow for the edges of theimages represented by the sensor data to be aligned with a centrallocation on the virtual sphere for preserving the most informativepixels.

The projected image may be applied to the DNN—e.g., a pre-trained DNN—to detect objects, features, and/or semantic information correspondingthereto. The output of the DNN, in embodiments, may be used toaccurately track objects as feature detection may be preserved at theedges of the images captured by wide field of view sensors—e.g.,portions of the images that, without stereographic projection, may bethe most distorted and thus the most difficult to make predictions withrespect to.

Once the features and/or objects are detected, the locationscorresponding thereto may be converted to their respective world-spacelocations. This may be accomplished using calibration informationcorresponding to the sensors, and may be based on adjustments (e.g.,vertical rotation) to the sensor data during processing. As a result,the original mapping of the image-space locations to the world-spacelocations from the unprocessed sensor data may be recovered in order toprepare the outputs of the DNN for use by a vehicle in performing one ormore operations.

With reference to FIG. 1A, FIG. 1A is an example data flow diagramillustrating an example process 100 for detecting features of avehicle's environment using outputs from one or more sensors of thevehicle, in accordance with some embodiments of the present disclosure.It should be understood that this and other arrangements describedherein are set forth only as examples, and the ordering of thecomponents and/or processes may be adjusted without departing from thescope of the present disclosure. Further, additional or alternativecomponents and/or processes others than those described herein may beimplemented.

The process 100 may include a stereographic projector 106 executing astereographic projection algorithm on one or more inputs (e.g., imagedata 102, other sensor data, etc.) and generating one or moreoutputs—such as projected image data 108. Further, the process 100 mayinclude one or more machine learning model(s) 110 (e.g., DNNs) receivingone or more inputs (e.g., the projected image data 108, with or withouta field of view adjustment from FOV adjuster 104) and generating one ormore outputs 112. In some examples, when used for training, the imagedata 102 may be referred to as training image data. Although the imagedata 102 is primarily discussed with respect to image datarepresentative of images, this is not intended to be limiting, and theimage data 102 may include other types of sensor data used for featureand/or object detection, such as LIDAR data, SONAR data, RADAR data,and/or the like—e.g., as generated by one or more sensors of the vehicle800 (FIGS. 8A-8D).

The process 100 may include generating and/or receiving image data 102from one or more sensors. The image data 102 may be received, as anon-limiting example, from one or more sensors of a vehicle (e.g.,vehicle 800 of FIGS. 8A-8C and described herein) captured by wide fieldof view sensors (e.g., sensors with fields of view greater than 120degrees). The image data 102 may be used by the vehicle, and within theprocess 100, to detect and/or classify objects or features to aidnavigation through the vehicle's environment in real-time or nearreal-time. The image data 102 may include, without limitation, imagedata 102 from any of the sensors of the vehicle including, for exampleand with reference to FIGS. 8A-8C, stereo camera(s) 868, wide-viewcamera(s) 870 (e.g., fisheye cameras), infrared camera(s) 872, surroundcamera(s) 874 (e.g., 360 degree cameras), and/or long-range and/ormid-range camera(s) 878. In some embodiments, in addition to oralternatively from the image data 102, sensor data from any number ofsensor types may be used, such as, without limitation, RADAR sensor(s)860, ultrasonic sensor(s) 862, LIDAR sensor(s) 864, and/or other sensortypes. In some embodiments, as described herein, the image data 102and/or the sensor data may be generated with sensors with sensory fieldsor fields of view that are greater than 120 degrees (e.g., 120 to 360degrees). As another example, the image data 102 may include virtualimage data generated from any number of sensors of a virtual vehicle orother virtual object. In such an example, the virtual sensors maycorrespond to a virtual vehicle or other virtual object in a simulatedenvironment (e.g., used for testing, training, and/or validating neuralnetwork performance), and the virtual image data may represent imagedata captured by the virtual sensors within the simulated or virtualenvironment.

In some embodiments, the image data 102 may include image datarepresenting an image(s), image data representing a video (e.g.,snapshots of video), and/or sensor data representing representations ofsensory fields of sensors (e.g., depth maps for LIDAR sensors, a valuegraph for ultrasonic sensors, etc.) captured by wide field of viewsensors (e.g., sensors with fields of view greater than 120 degrees).With respect to the image data 102, any type of image data format may beused, such as, for example and without limitation, compressed imagessuch as in Joint Photographic Experts Group (JPEG) orLuminance/Chrominance (YUV) formats, compressed images as framesstemming from a compressed video format such as H.264/Advanced VideoCoding (AVC) or H.265/High Efficiency Video Coding (HEVC), raw imagessuch as originating from Red Clear Blue (RCCB), Red Clear (RCCC), orother type of imaging sensor, and/or other formats. In addition, in someexamples, the image data 102 may be used within the process 100 withoutany pre-processing (e.g., in a raw or captured format), while in otherexamples, the image data 102 may undergo pre-processing (e.g., any oneor more of noise balancing, demosaicing, scaling, cropping,augmentation, white balancing, tone curve adjustment, etc., such asusing a sensor data pre-processor (not shown)). As used herein, theimage data 102 may reference unprocessed image data, pre-processed imagedata, or a combination thereof.

The image data 102 may include original images (e.g., as captured by oneor more image sensors), down-sampled images, up-sampled images, croppedor region of interest (ROI) images, otherwise augmented images, and/or acombination thereof. In some embodiments, the machine learning model(s)110 may be trained using the images (and/or other image data 102)captured using narrower field of view sensors (e.g., sensors with fieldsof view less than 120 degrees).

As an example of fields of view that are greater than 120 degrees, andwith reference to FIG. 2 , FIG. 2 illustrates fields of view of widefield of view sensors on a vehicle, in accordance with some embodimentsof the present disclosure. For example, vehicle 210 includes two widefield of view sensors, sensor 212 and sensor 214. Field of view 222 maycorrespond to the sensor 212 and may be approximately 190 degrees.Similarly, field of view 224 may correspond to the sensor 214 and may beapproximately 190 degrees. The sensor 212 and the sensor 214 may eachrepresent fisheye cameras (e.g., wide-view cameras 870 of the vehicle800) of the vehicle 210. In some examples, one or both of the sensor 212and the sensor 214 may be parking cameras used to assist the vehicle 210for parking, or may include side-view cameras used for assisting thevehicle 210 for lane changes, blind-spot monitoring, and/or the like.

Referring again to FIG. 1A, during deployment, the image data 102 may beapplied to the FOV adjuster 104, for example, to compute updated imagedata to be applied to the stereographic projector 106. In some examples,the image data 102 may undergo pre-processing to virtually adjust thefield of view of the wide field of view sensor to generate the updatedimage data such that the most distorted—and potentially informative andimportant—areas of the images (e.g., edges) may be relocated to areas ofthe virtual sphere where the pixel information is most preserved duringprojection. For example, images captured using narrower field of viewsensors may have minimal distortion near the edges of the field of view(e.g., as illustrated in image 310 of FIG. 3 ). However, images capturedusing higher field of view sensors often have heavy distortions at theedges, where there is lesser information available per pixel (e.g., asillustrated with in image 330 of FIG. 3 ).

For example, the FOV adjuster 104 may adjust the virtual field of viewof the wide field of view sensor in a vertical direction—e.g., up ordown. Due to the mounting angle of the sensors (e.g., facing downwardfor parking sensors and side-view sensors, facing upward forintersection analysis sensors, etc.), the field of view of the sensorsmay not be ideal for each implementation. For a non-limiting example, aparking sensor angled 40 degrees toward the ground may not have a fieldof view that is ideal for detecting vehicles in adjacent lanes or parkedalong a side of a street. However, it may still be advantageous toleverage the image data generated by the parking sensor angled downwardfor object or feature detection tasks. As such, the image data may beupdated to represent a virtual field of view of the parking sensor thatis angled at less than 40 degrees toward a ground surface—e.g., to 20degrees, or 0 degrees. In some embodiments, as described herein, thefield of view may be virtually adjusted such that a virtual center ofthe sensor substantially aligns with a horizon of the real world. Insuch examples, the virtual center of the images in the image data 102may be moved vertically—e.g., upwards or downwards—by rotating a virtualsphere by a predetermined degree. The virtual sphere may be used as avirtual field of view of the wide field of view sensor used to capturethe image data 102, and the updated image data representing thevirtually adjusted field of view may be applied to the stereographicprojector 106.

In some examples, the predetermined degree may be based on a sensorcalibration and may be, as a non-limiting example, between 20 and 60degrees. For example, the FOV adjuster 104 may determine the degree ofrotation of the virtual sphere based on the location and angle of thesensor on the vehicle 800. In some examples, the virtual adjustment maybe generated by rotating rays that form the original image of the imagedata 102 by the predetermined degree to generate the updated image.Aligning the virtual center of the wide field of view sensor with thehorizon may allow for the edges of the images in the image data 102 tobe aligned with a central location on the virtual sphere for preservinginformation where the most distortion is present in the un-adjustedimage.

As another example, and with reference to FIG. 3 , FIG. 3 illustratesimages captured using three sensors with different fields of view, inaccordance with some embodiments of the present disclosure. Image 310 isan image captured using a sensor having a horizontal field of view ofsixty degrees. As can be seen in image 310, there are substantially nodistortions in the image. Most machine learning models (e.g., machinelearning model(s) 110) are trained to detect features using trainingimages with a similar field of view as image 310. The machine learningmodels may be able to accurately and efficiently detect features inimages such as image 310 due to the lack of distortion. Similarly, image320 is an image captured using a sensor having a horizontal field ofview of approximately 120 degrees. As the field of view increases, sodoes distortion at the edges of the images captured. As can be seen, theimage 320 includes distortion on the right and left edges of the image.However, the image 320 may be almost entirely or substantiallyrectilinear, and the machine learning models may still be able to detectfeatures accurately throughout the image 320 because information loss isstill minimal in image 320. Image 330 is an image captured using asensor having a field of view of 190 degrees. Pixels at the left andright edges of the image exhibit a higher level of distortion than thepixels in the middle of the image 330. Where machine learning models,such as machine learning model(s) 110, are trained using images capturedby narrower field of view sensors (e.g., sensors with field of view lessthan 120 degrees), the machine learning models may not be able to detectfeatures in the distorted regions, such as edges of the image 330,without pre-processing. As a result, the process 100 may be used topre-process the images captured using fields of view closer to thatrepresented in the image 330 such that the machine learning model(s) 110(e.g., pre-trained on lower field of view images and/or trained onadjusted field of view or projected images) may accurately makepredictions with respect to the image 330.

As a further example, and with respect to FIG. 4 , FIG. 4 illustrates avirtually adjusted image adjusted using the FOV adjuster 104, inaccordance with some embodiments of the present disclosure. Image 410may be an original image captured by a high field of view (e.g., greaterthan 120 degrees) sensor 440 of a vehicle (e.g., the vehicle 800). TheFOV adjuster 104 of FIG. 1A may be used to virtually adjust a field ofview 444A of the wide field of view sensor 440 that captured the image410 to generate an updated image 422 such that the most distorted—andpotentially informative—areas of the images (e.g., edges 412 and 414)may be relocated to areas (e.g., areas 422 and 424) of the virtualsphere where the pixel information is most preserved during projection.The updated image 420 may be generated by vertically adjusting the fieldof view 444A of the sensor 440 to an updated field of view 444B suchthat the virtual center of the sensor is virtually aligned with adifferent region in the real world than the actual alignment of thesensor in the real world—e.g., such as the horizon 426 of the realworld. As can be seen, areas 422 and 424 of the updated image 420preserve the pixel information from the edges 412 and 414 of the image410. For example, the image 410 may captured by the wide field of viewsensor 440 such that the center of the sensor is facing down towards thestreet or ground plane 442, and the virtual center of the sensor afteradjustment in image 420 is facing more towards a horizon 426 (e.g., anintersection of a road or ground plane 422 with a sky in the distance).As such, by rotating the virtual center of the sensor upwards, the areas422 and 424 with the most distortion in the image 410 may be updatedsuch that the pixel information is preserved during projection using thestereographic projector 106.

Referring again to FIG. 1A, the updated image data (e.g., after field ofview adjustment) and/or the image data 102 may be applied to thestereographic projector 106 that is trained and/or programmed togenerate projected image data 108. The stereographic projector 106 mayexecute a stereographic projection algorithm, a gnomonic projectionalgorithm, and/or another type of projection algorithm. Thestereographic projector 106 may project pixels of the image data 102onto a two-dimensional (2D) projection plane (e.g., target plane) basedon ray formulation over a virtual sphere depicting a field of view ofthe wide field of view sensor used to capture the image data 102. Avirtual sphere may be used as the field of view of the wide field ofview sensor, and each pixel of the image data 102 may be projected ontothe 2D projection plane. The projected image data 108 may berepresentative of a projected image.

In some examples, the stereographic projector 106 may use a gnomonicprojection algorithm (and may alternatively be referred to as a “gnomicprojector” or “projector”) to generate the projected image data 108. Acenter of the virtual sphere may be used as a center of the projectionto project the image data 102 onto the 2D projection plane. For eachpixel of the 2D projection plane (e.g., projected image data 108), apoint on the virtual sphere may be determined to be projected onto thatpixel based on the intersection of a virtual line between the center ofthe projection and the pixel on the target plane with a point (e.g.,pixel) on the virtual sphere. In this way, every pixel on the targetplane or the projected image data 108 may correspond to a pixel sampledon the original image of the image data 102. However, gnomonicprojection algorithm may fail to project pixels in fields of viewgreater than 90 degrees, leaving the rest of the pixels of an imagecaptured from a sensor with a field of view greater than 90 degreesoutside the 2D projection plane as the projections for those pixels ofthe image data 102 may reach to infinity without intersecting the 2Dprojection plane. In such examples, subsets of the image data 102 may beprojected onto separate 2D projection planes to account for a widerfield of view (e.g., field of view greater than 90 degrees). Theseparate 2D projection planes may be used as projected image data 108 tobe applied to machine learning model(s) 110 to detect features and/orobjects. However, such partial and multiple projection may becomputationally expensive as at least four projection planes may berequired to cover the entire field of view of the image data 102.Further, a single feature or object may end up with portions representedacross different projected planes, thereby resulting in difficulty forthe machine learning model(s) 110 to predict such features and/orobjects.

In other examples, the stereographic projector 106 may use astereographic projection algorithm to generate the projected image data108 by projecting pixels of an image of the image data 102 or theupdated image data onto a single 2D projection plane. In such examples,the virtual sphere may be used as a virtual field of view of the sensor,and the lowest (e.g., vertically lowest) point on the virtual sphere maybe used as the center of the projection to project image data 102 ontothe 2D projection plane (e.g., as illustrated in FIGS. 1A and 1B). Foreach pixel of the 2D projection plane, a point on the virtual sphere maybe determined to be projected onto that pixel based on the intersectionof a virtual line between the center of the projection and the pixel onthe 2D projection plane with a point (e.g., pixel) on the virtualsphere. In this way, every pixel on the 2D projection plane or theprojected image of the projected image data 108 may correspond to apixel sampled on the original image of the image data 102 and/or theupdated image data. By generating a projected image in this way, eachoriginal image represented by the image data 102 and/or the updatedimage represented by the updated image data may be fully captured on atwo-dimensional plane such that the projected image is invertible, wherea feature detected by the machine learning model(s) 110 on the projectedimage may be retraced to the original image of image data 102 using aray formulation to determine a location of the feature on the originalimage of the image data 102. As non-limiting examples, FIG. 1Billustrates—among other things—how an original image may be projectedonto a 2D projection plane, using a virtual sphere, to generateprojected image data 108.

Further, the stereographic projector 106 may use the stereographicprojection algorithm to project images of image data 102 captured bywide field of view sensors onto a single plane, thereby comparativelyreducing the computational expense compared to gnomonic projectiontechniques that divide the images into various portions and subsequentlyproject the divided portions on multiple planes. The projected image inprojected image data 102 may include a planar view of the originalimages of the image data 102 captured by sensors with fields of viewbetween 120 degrees and 360 degrees by preserving areas of interest thathad the most distortion in the original, un-projected image.Specifically, the FOV projector 104 may move the distorted portions(e.g., edges) of the image data 102 to positions in the updated imagedata such that the distorted portions in the updated image data arelocated in areas where the stereographic projector 106 is configured topreserve more information. While the non-distorted portions of the imagedata 102 may be located in regions where the stereographic projector 106may not be configured to preserve as much information, the image data102 itself includes more information in those areas (e.g., center) ofthe image such that the machine learning model(s) 110 may still be ableto accurately predict features in such regions even with the informationloss. For example, with reference to FIG. 5 , object (or vehicle) 514may appear distorted after projection but, due to the number of pixelscorresponding to the object 514, the object 514 may still be accuratelydetected and/or classified by the machine learning model(s) 110.

As an example of stereographic projection, and with reference to FIG.1B, FIG. 1B illustrates how an original image may be projected onto a 2Dprojection plane, using a virtual sphere, to generate a projected image,in accordance with some embodiments of the present disclosure. A virtualsphere 120 is used as a virtual field of view of the sensor thatcaptured an image represented in the outline of the sphere, and thelowest (e.g., vertically lowest) point on the virtual sphere is used asthe center 122 of the projection to the image onto the target plane 118.For each pixel of the target plane, a point on the virtual sphere isdetermined to be projected onto that pixel based on the intersection ofa virtual line between the center 122 of the projection and the pixel onthe target plane 118 with a point (e.g., pixel) on the virtual sphere.For example, point 128 on the virtual sphere 120 is determined to beprojected onto pixel 130 of the target plane 118 based on theintersection of the virtual line 126 between the center 122 and thepixel 130. Similarly, points 134, 140, 144, and 150 on the virtualsphere are projected onto pixels 136, 140, 146, and 152, respectively,of the target plane 118 based on the intersections of the virtual lines132, 138, 142, and 148, respectively, between the center 122 and therespective pixels. As such, every pixel on the target plane 118 maycorrespond to a pixel sampled on the original image to generate aprojected image.

Referring again to FIG. 1A, the projected image data 108 may be appliedto a machine learning model(s) 110 trained to detect output(s) 112 fromthe image data 102—e.g., after field of view adjustments and/orprojection. The machine learning model(s) 110 may use the projectedimage data 108 to compute the output(s) 112, which may be applied to adecoder or one or more post-processing components (e.g., outputconverter) to generate information regarding the environment of thevehicle 800. The machine learning model(s) may be trained to identifyareas of interest (e.g., as one of the output(s) 112) pertaining to theenvironment of the vehicle 800. For example, the areas of interest, andsubsequently the output(s) 112, may include objects (e.g., vehicles,pedestrians, stop signs, etc.), features (e.g., raised pavement markers,rumble strips, colored lane dividers, sidewalks, cross-walks, turn-offs,road layouts, etc.), and/or semantic information (e.g., classifications,wait conditions, object types, lane types, etc.) pertaining thereto. Insome examples, the machine learning model(s) 110 may further be trainedto determine image-space locations of the detected object or features.In some embodiments, as described herein, the machine learning model(s)110 may be trained (e.g., pre-trained) with images or other sensor datarepresentations captured from narrower field of view sensors (e.g.,sensors with a field of view less than 120 degrees).

Although examples are described herein with respect to using deep neuralnetworks (DNNs), and specifically convolutional neural networks (CNNs),as the machine learning model(s) 110, this is not intended to belimiting. For example, and without limitation, the machine learningmodel(s) 110 may include any type of machine learning model, such as amachine learning model(s) using linear regression, logistic regression,decision trees, support vector machines (SVM), Naïve Bayes, k-nearestneighbor (Knn), K means clustering, random forest, dimensionalityreduction algorithms, gradient boosting algorithms, neural networks(e.g., auto-encoders, convolutional, recurrent, perceptrons, long/shortterm memory/LSTM, Hopfield, Boltzmann, deep belief, deconvolutional,generative adversarial, liquid state machine, etc.), lane detectionalgorithms, computer vision algorithms, and/or other types of machinelearning models.

As an example, such as where the machine learning model(s) 110 include aCNN, the machine learning model(s) 110 may include any number of layers.One or more of the layers may include an input layer. The input layermay hold values associated with the image data 102 and/or projectedimage data 108 (e.g., before or after post-processing). For example,when the image data 102 and/or the projected image data 108 is an image,the input layer may hold values representative of the raw pixel valuesof the image(s) as a volume (e.g., a width, a height, and color channels(e.g., RGB), such as 32×32×3).

One or more layers may include convolutional layers. The convolutionallayers may compute the output of neurons that are connected to localregions in an input layer, each neuron computing a dot product betweentheir weights and a small region they are connected to in the inputvolume. A result of the convolutional layers may be another volume, withone of the dimensions based on the number of filters applied (e.g., thewidth, the height, and the number of filters, such as 32×32×12, if 12were the number of filters).

One or more of the layers may include a rectified linear unit (ReLU)layer. The ReLU layer(s) may apply an elementwise activation function,such as the max (0, x), thresholding at zero, for example. The resultingvolume of a ReLU layer may be the same as the volume of the input of theReLU layer.

One or more of the layers may include a pooling layer. The pooling layermay perform a down sampling operation along the spatial dimensions(e.g., the height and the width), which may result in a smaller volumethan the input of the pooling layer (e.g., 16×16×12 from the 32×32×12input volume).

One or more of the layers may include one or more fully connectedlayer(s). Each neuron in the fully connected layer(s) may be connectedto each of the neurons in the previous volume. The fully connected layermay compute class scores, and the resulting volume may be 1×1×number ofclasses. In some examples, the CNN may include a fully connectedlayer(s) such that the output of one or more of the layers of the CNNmay be provided as input to a fully connected layer(s) of the CNN. Insome examples, one or more convolutional streams may be implemented bythe machine learning model(s) 110, and some or all of the convolutionalstreams may include a respective fully connected layer(s).

In some non-limiting embodiments, the machine learning model(s) 110 mayinclude a series of convolutional and max pooling layers to facilitateimage feature extraction, followed by multi-scale dilated convolutionaland up-sampling layers to facilitate global context feature extraction.

Although input layers, convolutional layers, pooling layers, ReLUlayers, and fully connected layers are discussed herein with respect tothe machine learning model(s) 110, this is not intended to be limiting.For example, additional or alternative layers may be used in the machinelearning model(s) 110, such as normalization layers, SoftMax layers,and/or other layer types.

In embodiments where the machine learning model(s) 110 includes a CNN,different orders and numbers of the layers of the CNN may be useddepending on the embodiment. In other words, the order and number oflayers of the machine learning model(s) 110 is not limited to any onearchitecture.

In addition, some of the layers may include parameters (e.g., weightsand/or biases), such as the convolutional layers and the fully connectedlayers, while others may not, such as the ReLU layers and poolinglayers. In some examples, the parameters may be learned by the machinelearning model(s) 110 during training. Further, some of the layers mayinclude additional hyper-parameters (e.g., learning rate, stride,epochs, etc.), such as the convolutional layers, the fully connectedlayers, and the pooling layers, while other layers may not, such as theReLU layers. The parameters and hyper-parameters are not to be limitedand may differ depending on the embodiment.

As an example of the outputs 112, and with reference to FIG. 5 , FIG. 5illustrates object detections in an image using a neural network, inaccordance with some embodiments of the present disclosure. Image 500may represent an image captured by a wide field of view sensor (e.g., asensor with a field of view greater than 120 degree, fisheye camera,wide-view camera 870, surround camera 874, parking assist camera, etc.)of a vehicle, such as the vehicle 800 of FIGS. 8A-8D. The machinelearning model(s) 110 of FIG. 1A, as described herein, may be used todetect objects 512, 514, and 516 in the image 500. The stereographicprojector 106 may be used to project the image 500 onto a 2D targetplane prior to applying the projected image data 108 of FIG. 1B to themachine learning model(s) 110. In some non-limiting embodiments, themachine learning model(s) 110 may be trained using images captured fromnarrower field of view sensors (e.g., sensors with fields of view lessthan 120 degrees). In this way, existing machine learning models may beleveraged to detect features in images captured from a different fieldof view than the training images without requiring re-training of themachine learning model(s) 110. Further, as a result of the adjustedfields of view and/or the projection, features and/or objects may bedetected even in the distorted regions of the original images prior tofield of view adjustment and/or projection—e.g., the regions of theimage corresponding to the objects (e.g., vehicles) 512 and 516. Assuch, the portions of the original image that may have the mostdistortion but represent important information—e.g., locations ofobjects 512 and 516—may be less distorted and the portions of theoriginal image without distortion (e.g., portions corresponding to theobject 514) may have more distortion but still maintain enough pixelsrepresentative thereof that the machine learning model(s) 110 may stillaccurately predict the outputs 112 corresponding thereto.

Referring again to FIG. 1A, in some examples, the output(s) 112 may beapplied to an output converter 114 to post-process the output(s) 112 ofthe machine learning model(s) 110. In some examples, once the featuresare detected in output(s) 112 of the machine learning model(s) 110, thelocations corresponding to the features may be converted or mapped totheir respective world-space locations. The output converter 114 may usecalibration information corresponding to the sensors (e.g., intrinsicand/or extrinsic parameters, such as a sensor model, a location andorientation of the sensor on the vehicle 800, focal length, lensdistortion, pose, etc.) that captured the image data 102 and/or theadjustments (e.g., vertical rotation) made by the FOV adjuster 104 tothe image data 102 during processing to convert the image-spacelocations from the output(s) 112 to world-space locations. As a result,the original mapping of the image-space locations to the world-spacelocations from the unprocessed sensor data may be recovered in order toprepare the output(s) 112 of the machine learning model(s) 110 for useby control component(s) 116 of the vehicle 800 in performing one or moreoperations.

In some embodiments, the output(s) 112 of the machine learning model(s)110 may be used to accurately track objects. For example, because thefeature detections may be preserved at the edges of the images of theimage data 102 captured by wide field of view sensors that areoriginally distorted, the objects or features may be accurately trackedthroughout the entire field of view (e.g., including portions of theimages that, without stereographic projection, may be the most distortedand thus the most difficult to make predictions with respect to). Theoutput(s) 112, including features and/or objects in the environment, maybe tracked in subsequent images of the image data 102 and, in someexamples, temporal analysis may be used to track the features, from whenthey are detected at one edge of a field of view through to another edgeof the field of view. As such, where tracking is executed, the outputconverter 114 may use the real-world converted outputs of the objectsand/or features to generate a location or movement history correspondingthereto.

Once the output(s) 112 are determined and/or converted, this informationmay be passed to control component(s) 116 of the system to perform oneor more operations. For example, where the system is the vehicle 800,described herein, the output(s) 112 and/or converted outputs may bepassed to one or more layers of an autonomous driving software stack(e.g., a planning layer, a control layer, a world-model manager, aperception layer, an obstacle avoidance layer of the drive stack, anactuation layer of the drive stack, etc.) to determine an appropriatecontrol decision. As such, the control component(s) 116 may make controldecision that may include suggesting one or more of path planning,obstacle avoidance, and/or control decisions—such as where to stop, howfast to drive, what path to use to safely traverse the environment,where other vehicles or pedestrians may be located, and/or the like. Inany example, and with respect to autonomous or semi-autonomous driving,the control decisions may include any decisions corresponding to aperception layer of the drive stack, a world model management layer ofthe drive stack, a planning layer of the drive stack, a control layer ofthe drive stack, an obstacle avoidance layer of the drive stack, anactuation layer of the drive stack, and/or another layer, feature, orfunction of a drive stack. In some examples, the process 100 may beexecuted on any number of machine learning model(s) 110 operating withina system. For example, an autonomous driving software stack may rely onhundreds or thousands of machine learning model(s) 110 for effective andsafe operation, and any number of these may be subject to the process100 in order to ensure safe and effective operation while leveragingsensors having larger fields of view (e.g., greater than 120 degrees).As such, as described herein, the process 100 may be separatelyperformed for any number of different operations corresponding to one ormore layers of the drive stack and using any number of machine learningmodel(s) 110. As an example, a first detection may be determined forobject detection operations with respect to the perception layer of thedrive stack using a first machine learning model, and a second detectionmay be determined for path planning with respect to the planning layerof the drive stack using a second machine learning model trained forregressing on lane lines.

Now referring to FIGS. 6 and 7 , each block of methods 600 and 700,described herein, comprises a computing process that may be performedusing any combination of hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. The methods 600 and 700 may also beembodied as computer-usable instructions stored on computer storagemedia. The methods 600 and 700 may be provided by a standaloneapplication, a service or hosted service (standalone or in combinationwith another hosted service), or a plug-in to another product, to name afew. In addition, the methods 600 and 700 are described, by way ofexample, with respect to the process 100 of FIG. 1A and the vehicle 800of FIGS. 8A-8D. However, these methods may additionally or alternativelybe executed by any one system or within any one process, or anycombination of systems and processes, including, but not limited to,those described herein.

Now referring to FIG. 6 , FIG. 6 is a flow diagram showing a method 600for detecting features in images captured by wide field of view sensorsusing an existing neural network trained on images captured usingnarrower field of view sensors, in accordance with some embodiments ofthe present disclosure. The method 600, at block B602, includesreceiving image data representative of an image generated using a firstimage sensor having a field of view. For example, the image data 102 maybe received, where the image data 102 represents an image captured froma wide field of view sensor (e.g., a sensor having a field of view of120 degree or more).

The method 600, at block B604, includes applying the image data to astereographic projection algorithm to generate projected image datarepresentative of a projected image. For example, the image data 102 maybe applied to the stereographic projector 106 that executes astereographic projection algorithm to generate the projected image data108 representative of a projected image generated by projecting pixelsof the image data 102 onto a 2D projection or target plane.

The method 600, at block B606, includes applying the projected imagedata to a neural network, the neural network trained to detect featuresrepresented by training image data representative of images generatedusing one or more second image sensors having fields of view less thanthe field of view. For example, the projected image data 108 may beapplied to the machine learning model(s) 110 that is trained to detectand/or classify features and/or objects represented by training imagedata representative of images generated using narrower field of viewsensors (e.g., sensors having fields of view less than 120 degrees).

The method 600, at block B608, includes computing, using the neuralnetwork and based at least in part on the projected image data, datarepresentative of feature detections corresponding to one or morefeatures. For example, the machine learning model(s) 110 may computeoutput(s) 112 based on the projected image data 108. The output(s) 112may include data representative of feature and/or object detectionscorresponding to one or more features in the projected image data 108.

Referring now to FIG. 7 , FIG. 7 is a flow diagram showing a method 700for detecting features in images captured by wide field of view sensors,in accordance with some embodiments of the present disclosure. Themethod 700, at block B702, includes receiving image data representativeof an image generated using a first image sensor having a field of viewof greater than or equal to 120 degrees. For example, the image data 102may be received, where the image data represents an image captured froma wide field of view sensor (e.g., a sensor having a field of view of120 degree or more).

The method 700, at block B704, includes virtually adjusting the field ofview of the first image sensor. For example, the FOV adjuster 104 mayvirtually adjust the field of view of the image sensor used to capturethe image data 102.

The method 700, at block B706, includes generating, based at least inpart on the image data, updated image data corresponding to thevirtually adjusted field of view. For example, updated imaged data maybe generated by the FOV adjuster 104 that corresponds to the virtuallyadjusted field of view.

The method 700, at block B708, includes applying the updated image datato a stereographic projection algorithm to generate projected image datarepresentative of a projected image. For example, the updated image datamay be applied to the stereographic projector 106 that executes astereographic projection algorithm to generate projected image data 108representative of a projected image generated by projecting pixels ofthe updated image data onto a 2D projection or target plane.

The method 700, at block B710, includes applying the projected imagedata to one of a machine learning model or a computer vision algorithm.For example, the projected image data 108 may be applied to the machinelearning model(s) 110.

The method 700, at block 712, includes computing, using one of themachine learning model or the computer vision algorithm and based atleast in part on the projected image data, data representative offeature detections corresponding to one or more features. For example,the machine learning model(s) 110 may compute output(s) 112 based on theprojected image data 108. The output(s) 112 may include datarepresentative of feature and/or object detections or classificationscorresponding to one or more features and/or objects represented by theprojected image data 108.

Example Autonomous Vehicle

FIG. 8A is an illustration of an example autonomous vehicle 800, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 800 (alternatively referred to herein as the “vehicle800”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,and/or another type of vehicle (e.g., that is unmanned and/or thataccommodates one or more passengers). Autonomous vehicles are generallydescribed in terms of automation levels, defined by the National HighwayTraffic Safety Administration (NHTSA), a division of the US Departmentof Transportation, and the Society of Automotive Engineers (SAE)“Taxonomy and Definitions for Terms Related to Driving AutomationSystems for On-Road Motor Vehicles” (Standard No. J3016-201806,published on Jun. 15, 2018, Standard No. J3016-201609, published on Sep.30, 2016, and previous and future versions of this standard). Thevehicle 800 may be capable of functionality in accordance with one ormore of Level 3-Level 5 of the autonomous driving levels. For example,the vehicle 800 may be capable of conditional automation (Level 3), highautomation (Level 4), and/or full automation (Level 5), depending on theembodiment.

The vehicle 800 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 800 may include a propulsion system850, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 850 may be connected to a drive train of the vehicle800, which may include a transmission, to enable the propulsion of thevehicle 800. The propulsion system 850 may be controlled in response toreceiving signals from the throttle/accelerator 852.

A steering system 854, which may include a steering wheel, may be usedto steer the vehicle 800 (e.g., along a desired path or route) when thepropulsion system 850 is operating (e.g., when the vehicle is inmotion). The steering system 854 may receive signals from a steeringactuator 856. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 846 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 848 and/or brakesensors.

Controller(s) 836, which may include one or more system on chips (SoCs)804 (FIG. 8C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle800. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 848, to operate thesteering system 854 via one or more steering actuators 856, to operatethe propulsion system 850 via one or more throttle/accelerators 852. Thecontroller(s) 836 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 800. The controller(s) 836 may include a first controller 836for autonomous driving functions, a second controller 836 for functionalsafety functions, a third controller 836 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 836 forinfotainment functionality, a fifth controller 836 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 836 may handle two or more of the abovefunctionalities, two or more controllers 836 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 836 may provide the signals for controlling one ormore components and/or systems of the vehicle 800 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 858 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 860, ultrasonic sensor(s) 862, LIDARsensor(s) 864, inertial measurement unit (IMU) sensor(s) 866 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 896, stereo camera(s) 868, wide-view camera(s) 870(e.g., fisheye cameras), infrared camera(s) 872, surround camera(s) 874(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 898,speed sensor(s) 844 (e.g., for measuring the speed of the vehicle 800),vibration sensor(s) 842, steering sensor(s) 840, brake sensor(s) (e.g.,as part of the brake sensor system 846), and/or other sensor types.

One or more of the controller(s) 836 may receive inputs (e.g.,represented by input data) from an instrument cluster 832 of the vehicle800 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 834, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle800. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 822 of FIG. 8C), location data(e.g., the vehicle's 800 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 836,etc. For example, the HMI display 834 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 800 further includes a network interface 824 which may useone or more wireless antenna(s) 826 and/or modem(s) to communicate overone or more networks. For example, the network interface 824 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 826 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 8B is an example of camera locations and fields of view for theexample autonomous vehicle 800 of FIG. 8A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle800.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 800. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 820 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RB GC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or an RBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 800 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 836 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (“LDW”), Autonomous Cruise Control(“ACC”), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 870 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.8B, there may any number of wide-view cameras 870 on the vehicle 800. Inaddition, long-range camera(s) 898 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 898 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 868 may also be included in a front-facingconfiguration. The stereo camera(s) 868 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 868 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 868 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 800 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 874 (e.g., four surround cameras 874 asillustrated in FIG. 8B) may be positioned to on the vehicle 800. Thesurround camera(s) 874 may include wide-view camera(s) 870, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 874 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 800 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 898,stereo camera(s) 868), infrared camera(s) 872, etc.), as describedherein.

FIG. 8C is a block diagram of an example system architecture for theexample autonomous vehicle 800 of FIG. 8A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 800 in FIG.8C are illustrated as being connected via bus 802. The bus 802 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 800 used to aid in control of various features and functionalityof the vehicle 800, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 802 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 802, this is notintended to be limiting. For example, there may be any number of busses802, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses802 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 802 may be used for collisionavoidance functionality and a second bus 802 may be used for actuationcontrol. In any example, each bus 802 may communicate with any of thecomponents of the vehicle 800, and two or more busses 802 maycommunicate with the same components. In some examples, each SoC 804,each controller 836, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle800), and may be connected to a common bus, such the CAN bus.

The vehicle 800 may include one or more controller(s) 836, such as thosedescribed herein with respect to FIG. 8A. The controller(s) 836 may beused for a variety of functions. The controller(s) 836 may be coupled toany of the various other components and systems of the vehicle 800, andmay be used for control of the vehicle 800, artificial intelligence ofthe vehicle 800, infotainment for the vehicle 800, and/or the like.

The vehicle 800 may include a system(s) on a chip (SoC) 804. The SoC 804may include CPU(s) 806, GPU(s) 808, processor(s) 810, cache(s) 812,accelerator(s) 814, data store(s) 816, and/or other components andfeatures not illustrated. The SoC(s) 804 may be used to control thevehicle 800 in a variety of platforms and systems. For example, theSoC(s) 804 may be combined in a system (e.g., the system of the vehicle800) with an HD map 822 which may obtain map refreshes and/or updatesvia a network interface 824 from one or more servers (e.g., server(s)878 of FIG. 8D).

The CPU(s) 806 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 806 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 806may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 806 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 806 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)806 to be active at any given time.

The CPU(s) 806 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 806may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 808 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 808 may be programmable and may beefficient for parallel workloads. The GPU(s) 808, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 808 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 808 may include at least eight streamingmicroprocessors. The GPU(s) 808 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 808 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 808 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 808 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 808 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 808 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 808 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 808 to access the CPU(s) 806 page tables directly. Insuch examples, when the GPU(s) 808 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 806. In response, the CPU(s) 806 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 808. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 806 and the GPU(s) 808, thereby simplifying the GPU(s) 808programming and porting of applications to the GPU(s) 808.

In addition, the GPU(s) 808 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 808 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 804 may include any number of cache(s) 812, including thosedescribed herein. For example, the cache(s) 812 may include an L3 cachethat is available to both the CPU(s) 806 and the GPU(s) 808 (e.g., thatis connected both the CPU(s) 806 and the GPU(s) 808). The cache(s) 812may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 804 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 800—such as processingDNNs. In addition, the SoC(s) 804 may include a floating point unit(s)(FPU(s))—or other math coprocessor or numeric coprocessor types—forperforming mathematical operations within the system. For example, theSoC(s) 104 may include one or more FPUs integrated as execution unitswithin a CPU(s) 806 and/or GPU(s) 808.

The SoC(s) 804 may include one or more accelerators 814 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 804 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 808 and to off-load some of the tasks of theGPU(s) 808 (e.g., to free up more cycles of the GPU(s) 808 forperforming other tasks). As an example, the accelerator(s) 814 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 814 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 808, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 808 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 808 and/or other accelerator(s) 814.

The accelerator(s) 814 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 806. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 814 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 814. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 804 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 814 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 866 output thatcorrelates with the vehicle 800 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 864 or RADAR sensor(s) 860), amongothers.

The SoC(s) 804 may include data store(s) 816 (e.g., memory). The datastore(s) 816 may be on-chip memory of the SoC(s) 804, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 816 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 812 may comprise L2 or L3 cache(s) 812. Reference to thedata store(s) 816 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 814, as described herein.

The SoC(s) 804 may include one or more processor(s) 810 (e.g., embeddedprocessors). The processor(s) 810 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 804 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 804 thermals and temperature sensors, and/ormanagement of the SoC(s) 804 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 804 may use thering-oscillators to detect temperatures of the CPU(s) 806, GPU(s) 808,and/or accelerator(s) 814. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 804 into a lower powerstate and/or put the vehicle 800 into a chauffeur to safe stop mode(e.g., bring the vehicle 800 to a safe stop).

The processor(s) 810 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 810 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 810 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 810 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 810 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 810 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)870, surround camera(s) 874, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 808 is not required tocontinuously render new surfaces. Even when the GPU(s) 808 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 808 to improve performance and responsiveness.

The SoC(s) 804 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 804 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 804 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 804 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 864, RADAR sensor(s) 860,etc. that may be connected over Ethernet), data from bus 802 (e.g.,speed of vehicle 800, steering wheel position, etc.), data from GNSSsensor(s) 858 (e.g., connected over Ethernet or CAN bus). The SoC(s) 804may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 806 from routine data management tasks.

The SoC(s) 804 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 804 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 814, when combined with the CPU(s) 806, the GPU(s) 808,and the data store(s) 816, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 820) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 808.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 800. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 804 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 896 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 804 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)858. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 862, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 818 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 804 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 818 may include an X86 processor,for example. The CPU(s) 818 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 804, and/or monitoring the statusand health of the controller(s) 836 and/or infotainment SoC 830, forexample.

The vehicle 800 may include a GPU(s) 820 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 804 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 820 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 800.

The vehicle 800 may further include the network interface 824 which mayinclude one or more wireless antennas 826 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 824 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 878 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 800information about vehicles in proximity to the vehicle 800 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 800).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 800.

The network interface 824 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 836 tocommunicate over wireless networks. The network interface 824 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 800 may further include data store(s) 828 which may includeoff-chip (e.g., off the SoC(s) 804) storage. The data store(s) 828 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 800 may further include GNSS sensor(s) 858. The GNSSsensor(s) 858 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. Any number of GNSS sensor(s) 858 may be used, including, forexample and without limitation, a GPS using a USB connector with anEthernet to Serial (RS-232) bridge.

The vehicle 800 may further include RADAR sensor(s) 860. The RADARsensor(s) 860 may be used by the vehicle 800 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 860 may usethe CAN and/or the bus 802 (e.g., to transmit data generated by theRADAR sensor(s) 860) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 860 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 860 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 860may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 800 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 800 lane.

Mid-range RADAR systems may include, as an example, a range of up to 860m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 850 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 800 may further include ultrasonic sensor(s) 862. Theultrasonic sensor(s) 862, which may be positioned at the front, back,and/or the sides of the vehicle 800, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 862 may be used, and different ultrasonic sensor(s) 862 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 862 may operate at functional safety levels of ASILB.

The vehicle 800 may include LIDAR sensor(s) 864. The LIDAR sensor(s) 864may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 864 maybe functional safety level ASIL B. In some examples, the vehicle 800 mayinclude multiple LIDAR sensors 864 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 864 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 864 may have an advertised rangeof approximately 800 m, with an accuracy of 2 cm-3 cm, and with supportfor a 800 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 864 may be used. In such examples,the LIDAR sensor(s) 864 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 800.The LIDAR sensor(s) 864, in such examples, may provide up to a820-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)864 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 800. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)864 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 866. The IMU sensor(s) 866may be located at a center of the rear axle of the vehicle 800, in someexamples. The IMU sensor(s) 866 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 866 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 866 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 866 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 866 may enable the vehicle 800to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 866. In some examples, the IMU sensor(s) 866 and theGNSS sensor(s) 858 may be combined in a single integrated unit.

The vehicle may include microphone(s) 896 placed in and/or around thevehicle 800. The microphone(s) 896 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 868, wide-view camera(s) 870, infrared camera(s) 872,surround camera(s) 874, long-range and/or mid-range camera(s) 898,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 800. The types of cameras useddepends on the embodiments and requirements for the vehicle 800, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 800. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 8A and FIG. 8B.

The vehicle 800 may further include vibration sensor(s) 842. Thevibration sensor(s) 842 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 842 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 800 may include an ADAS system 838. The ADAS system 838 mayinclude a SoC, in some examples. The ADAS system 838 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 860, LIDAR sensor(s) 864, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 800 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 800 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 824 and/or the wireless antenna(s) 826 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 800), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 800, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 860, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle800 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 800 if the vehicle 800 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 800 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 860, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 800, the vehicle 800itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 836 or a second controller 836). For example, in someembodiments, the ADAS system 838 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 838may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 804.

In other examples, ADAS system 838 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 838 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 838indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 800 may further include the infotainment SoC 830 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 830 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 800. For example, the infotainment SoC 830 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 834, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 830 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 838,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 830 may include GPU functionality. The infotainmentSoC 830 may communicate over the bus 802 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 800. Insome examples, the infotainment SoC 830 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 836(e.g., the primary and/or backup computers of the vehicle 800) fail. Insuch an example, the infotainment SoC 830 may put the vehicle 800 into achauffeur to safe stop mode, as described herein.

The vehicle 800 may further include an instrument cluster 832 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 832 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 832 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 830 and theinstrument cluster 832. In other words, the instrument cluster 832 maybe included as part of the infotainment SoC 830, or vice versa.

FIG. 8D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 800 of FIG. 8A, inaccordance with some embodiments of the present disclosure. The system876 may include server(s) 878, network(s) 890, and vehicles, includingthe vehicle 800. The server(s) 878 may include a plurality of GPUs884(A)-884(H) (collectively referred to herein as GPUs 884), PCIeswitches 882(A)-882(H) (collectively referred to herein as PCIe switches882), and/or CPUs 880(A)-880(B) (collectively referred to herein as CPUs880). The GPUs 884, the CPUs 880, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 888 developed by NVIDIA and/orPCIe connections 886. In some examples, the GPUs 884 are connected viaNVLink and/or NVSwitch SoC and the GPUs 884 and the PCIe switches 882are connected via PCIe interconnects. Although eight GPUs 884, two CPUs880, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 878 mayinclude any number of GPUs 884, CPUs 880, and/or PCIe switches. Forexample, the server(s) 878 may each include eight, sixteen, thirty-two,and/or more GPUs 884.

The server(s) 878 may receive, over the network(s) 890 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 878 may transmit, over the network(s) 890 and to the vehicles,neural networks 892, updated neural networks 892, and/or map information894, including information regarding traffic and road conditions. Theupdates to the map information 894 may include updates for the HD map822, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 892, the updated neural networks 892, and/or the mapinformation 894 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 878 and/or other servers).

The server(s) 878 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Training may beexecuted according to any one or more classes of machine learningtechniques, including, without limitation, classes such as: supervisedtraining, semi-supervised training, unsupervised training,self-learning, reinforcement learning, federated learning, transferlearning, feature learning (including principal component and clusteranalyses), multi-linear subspace learning, manifold learning,representation learning (including spare dictionary learning),rule-based machine learning, anomaly detection, and any variants orcombinations therefor. Once the machine learning models are trained, themachine learning models may be used by the vehicles (e.g., transmittedto the vehicles over the network(s) 890, and/or the machine learningmodels may be used by the server(s) 878 to remotely monitor thevehicles.

In some examples, the server(s) 878 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 878 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 884, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 878 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 878 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 800. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 800, suchas a sequence of images and/or objects that the vehicle 800 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 800 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 800 is malfunctioning, the server(s) 878 may transmit asignal to the vehicle 800 instructing a fail-safe computer of thevehicle 800 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 878 may include the GPU(s) 884 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 9 is a block diagram of an example computing device(s) 900 suitablefor use in implementing some embodiments of the present disclosure.Computing device 900 may include an interconnect system 902 thatdirectly or indirectly couples the following devices: memory 904, one ormore central processing units (CPUs) 906, one or more graphicsprocessing units (GPUs) 908, a communication interface 910, input/output(I/O) ports 912, input/output components 914, a power supply 916, one ormore presentation components 918 (e.g., display(s)), and one or morelogic units 920.

Although the various blocks of FIG. 9 are shown as connected via theinterconnect system 902 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component 918, such as a display device, may be consideredan I/O component 914 (e.g., if the display is a touch screen). Asanother example, the CPUs 906 and/or GPUs 908 may include memory (e.g.,the memory 904 may be representative of a storage device in addition tothe memory of the GPUs 908, the CPUs 906, and/or other components). Inother words, the computing device of FIG. 9 is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.9 .

The interconnect system 902 may represent one or more links or busses,such as an address bus, a data bus, a control bus, or a combinationthereof. The interconnect system 902 may include one or more bus or linktypes, such as an industry standard architecture (ISA) bus, an extendedindustry standard architecture (EISA) bus, a video electronics standardsassociation (VESA) bus, a peripheral component interconnect (PCI) bus, aperipheral component interconnect express (PCIe) bus, and/or anothertype of bus or link. In some embodiments, there are direct connectionsbetween components. As an example, the CPU 906 may be directly connectedto the memory 904. Further, the CPU 906 may be directly connected to theGPU 908. Where there is direct, or point-to-point connection betweencomponents, the interconnect system 902 may include a PCIe link to carryout the connection. In these examples, a PCI bus need not be included inthe computing device 900.

The memory 904 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 900. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 904 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device900. As used herein, computer storage media does not comprise signalsper se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

The CPU(s) 906 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thecomputing device 900 to perform one or more of the methods and/orprocesses described herein. The CPU(s) 906 may each include one or morecores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.)that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 906 may include any type of processor, andmay include different types of processors depending on the type ofcomputing device 900 implemented (e.g., processors with fewer cores formobile devices and processors with more cores for servers). For example,depending on the type of computing device 900, the processor may be anAdvanced RISC Machines (ARM) processor implemented using ReducedInstruction Set Computing (RISC) or an x86 processor implemented usingComplex Instruction Set Computing (CISC). The computing device 900 mayinclude one or more CPUs 906 in addition to one or more microprocessorsor supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 906, the GPU(s) 908 maybe configured to execute at least some of the computer-readableinstructions to control one or more components of the computing device900 to perform one or more of the methods and/or processes describedherein. One or more of the GPU(s) 908 may be an integrated GPU (e.g.,with one or more of the CPU(s) 906 and/or one or more of the GPU(s) 908may be a discrete GPU. In embodiments, one or more of the GPU(s) 908 maybe a coprocessor of one or more of the CPU(s) 906. The GPU(s) 908 may beused by the computing device 900 to render graphics (e.g., 3D graphics)or perform general purpose computations. For example, the GPU(s) 908 maybe used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 908may include hundreds or thousands of cores that are capable of handlinghundreds or thousands of software threads simultaneously. The GPU(s) 908may generate pixel data for output images in response to renderingcommands (e.g., rendering commands from the CPU(s) 906 received via ahost interface). The GPU(s) 908 may include graphics memory, such asdisplay memory, for storing pixel data or any other suitable data, suchas GPGPU data. The display memory may be included as part of the memory904. The GPU(s) 908 may include two or more GPUs operating in parallel(e.g., via a link). The link may directly connect the GPUs (e.g., usingNVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch).When combined together, each GPU 908 may generate pixel data or GPGPUdata for different portions of an output or for different outputs (e.g.,a first GPU for a first image and a second GPU for a second image). EachGPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 906 and/or the GPU(s)908, the logic unit(s) 920 may be configured to execute at least some ofthe computer-readable instructions to control one or more components ofthe computing device 900 to perform one or more of the methods and/orprocesses described herein. In embodiments, the CPU(s) 906, the GPU(s)908, and/or the logic unit(s) 920 may discretely or jointly perform anycombination of the methods, processes and/or portions thereof. One ormore of the logic units 920 may be part of and/or integrated in one ormore of the CPU(s) 906 and/or the GPU(s) 908 and/or one or more of thelogic units 920 may be discrete components or otherwise external to theCPU(s) 906 and/or the GPU(s) 908. In embodiments, one or more of thelogic units 920 may be a coprocessor of one or more of the CPU(s) 906and/or one or more of the GPU(s) 908.

Examples of the logic unit(s) 920 include one or more processing coresand/or components thereof, such as Tensor Cores (TCs), Tensor ProcessingUnits(TPUs), Pixel Visual Cores (PVCs), Vision Processing Units (VPUs),Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs),Streaming Multiprocessors (SMs), Tree Traversal Units (TTUs), ArtificialIntelligence Accelerators (AIAs), Deep Learning Accelerators (DLAs),Arithmetic-Logic Units (ALUs), Application-Specific Integrated Circuits(ASICs), Floating Point Units (FPUs), input/output (I/O) elements,peripheral component interconnect (PCI) or peripheral componentinterconnect express (PCIe) elements, and/or the like.

The communication interface 910 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 900to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 910 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet orInfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet.

The I/O ports 912 may enable the computing device 900 to be logicallycoupled to other devices including the I/O components 914, thepresentation component(s) 918, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 900.Illustrative I/O components 914 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 914 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 900. Thecomputing device 900 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 900 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 900 to render immersive augmented reality or virtual reality.

The power supply 916 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 916 may providepower to the computing device 900 to enable the components of thecomputing device 900 to operate.

The presentation component(s) 918 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 918 may receivedata from other components (e.g., the GPU(s) 908, the CPU(s) 906, etc.),and output the data (e.g., as an image, video, sound, etc.).

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A method comprising: virtually rotating, based atleast on one or more orientations of one or more image sensors, one ormore fields of view corresponding to the one or more image sensors togenerate one or more images having one or more adjusted fields of view;projecting the one or more images onto one or more planes to generateone or more projected images; computing, using one or more neuralnetworks and based at least on at least one projected image of the oneor more projected images, one or more outputs; and performing one ormore operations using one or more machines based at least on the one ormore outputs.
 2. The method of claim 1, wherein the projecting isperformed using one or more of a stereographic projection algorithm or agnomonic projection algorithm.
 3. The method of claim 1, wherein thevirtually rotating aligns one or more locations corresponding to the oneor more image sensors with one or more identified regions in the one ormore fields of view.
 4. The method of claim 3, wherein the one or morelocations include a virtual center corresponding to the one or moreimage sensors, and at least one identified region of the one or moreidentified regions corresponds to a horizon in the one or more fields ofview.
 5. The method of claim 1, wherein the virtually rotating includesvirtually rotating the one or more fields of view by one or more anglesdetermined based at least on one or more mounting angles of the one ormore image sensors.
 6. The method of claim 1, wherein the virtuallyrotating includes virtually rotating the one or more fields of viewalong at least one or more vertical directions by one or morepredetermined degrees.
 7. The method of claim 1, wherein the virtuallyrotating includes virtually rotating one or more virtual spheresassociated with the one or more fields of view by one or more degrees.8. The method of claim 1, wherein the one or more neural networks aretrained using training image data representative of images generatedusing one or more second image sensors having one or more second fieldsof view that are less than the one or more fields of view.
 9. The methodof claim 1, wherein the one or more machines include a vehicle, and theone or more operations correspond to navigating the vehicle along adriving surface.
 10. A system comprising: one or more processing devicesto: generate updated image data corresponding to one or more firstfields of view, at least, by virtually rotating one or more secondfields of view corresponding to one or more image sensors based at leaston one or more orientations of the one or more image sensors; generateprojected image data representative of one or more projected imagesbased at least on applying the updated image data to one or more imageprojection algorithms; determine, using one or more machine learningmodels (MLMs) and based at least on the projected image data, outputdata; and perform one or more operations using one or more machinesbased at least on the output data.
 11. The system of claim 10, whereinthe one or more image projection algorithms include one or more of astereographic projection algorithm or a gnomonic projection algorithm.12. The system of claim 10, wherein the virtually, rotating includesvirtually rotating the one or more second fields of view tosubstantially align one or more locations corresponding to the one ormore image sensors with one or more regions in the one or more secondfields of view.
 13. The system of claim 10, wherein the virtuallyrotating includes virtually rotating the one or more second fields ofview by one or more angles that correspond to one or more mountingangles of the one or more image sensors.
 14. The system of claim 10,wherein the virtually rotating includes virtually rotating the one ormore second fields of view along at least one or more verticaldirections by one or more degrees.
 15. The system of claim 10, whereinthe system is comprised in at least one of: a control system for anautonomous or semi-autonomous machine; a perception system for anautonomous or semi-autonomous machine; a system for performingsimulation operations; a system for performing ray-tracing operations; asystem for performing deep learning operations; a system implementedusing a robot; a system for presenting at least one of virtual realitycontent or augmented reality content; or a system implemented at leastpartially using cloud computing resources.
 16. A processor comprising:one or more circuits to perform one or more operations using one or moremachines based at least on one or more outputs, the one or more outputscomputed based at least on one or more machine learning models (MLMs)processing data representative of at least one projected imagegenerated, at least, by virtually rotating one or more fields of viewcorresponding to one or more image sensors toward a horizon.
 17. Theprocessor of claim 16, wherein the at least one projected image isfurther generated, at least, by using one or more of a stereographicprojection algorithm or a gnomonic projection algorithm.
 18. Theprocessor of claim 16, wherein the virtually rotating includes virtuallyrotating the one or more fields of view to substantially align one ormore locations corresponding to the one or more image sensors with oneor more regions in the one or more fields of view, the one or moreregions including, at least, the horizon.
 19. The processor of claim 16,wherein the virtually rotating includes virtually rotating the one ormore fields of view by one or more angles that correspond to one or moremounting angles of the one or more image sensors.
 20. The processor ofclaim 16, wherein the processor is comprised in at least one of: acontrol system for an autonomous or semi-autonomous machine; aperception system for an autonomous or semi-autonomous machine; a systemfor performing simulation operations; a system for performingray-tracing operations; a system for performing deep learningoperations; a system implemented using a robot; a system for presentingat least one of virtual reality content or augmented reality content; ora system implemented at least partially using cloud computing resources.